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annotate TensorMappings.jl @ 178:64b9751b3cb2 boundary_conditions

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Fix error in getindex for TensorApplicationExpression
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Mon, 17 Jun 2019 10:41:20 +0200
parents 24779d423243
children
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1 module TensorMappings
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2 # Needs a better name ImplicitTensorMappings? Get rid of "Tensor" in the name_
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3
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4 abstract type TensorMapping{T,R,D} end
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6 range_dim(::TensorMapping{T,R,D}) where {T,R,D} = R
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7 domain_dim(::TensorMapping{T,R,D}) where {T,R,D} = D
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8 # range_size(::TensorMapping{T,R,D}, domain_size::NTuple{D,Integer}) where {T,R,D}
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9 # domain_size(::TensorMapping{T,R,D}, range_size::NTuple{R,Integer}) where {T,R,D}
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11 # apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T} =
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12 # apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T} =
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13 # Implementing apply_transpose and domain_size is only needed if you want to take transposes of the TensorMapping.
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14 # TODO: Think about boundschecking!
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16 abstract type TensorOperator{T,D} <: TensorMapping{T,D,D} end
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17 domain_size(::TensorOperator{T,D}, range_size::NTuple{D,Integer}) where {T,D} = range_size
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18 range_size(::TensorOperator{T,D}, domain_size::NTuple{D,Integer}) where {T,D} = domain_size
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22 # Allow using the ' operator:
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23 struct TensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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24 tm::TensorMapping{T,R,D}
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25 end
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26
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27 Base.adjoint(t::TensorMapping) = TensorMappingTranspose(t)
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28 # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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29 Base.adjoint(t::TensorMappingTranspose) = t.tm
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30
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31 apply(tm::TensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {T,R,D} = apply_transpose(tm.tm, v, I...)
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32 apply_transpose(tm::TensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,D} = apply(tm.tm, v, I...)
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33
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34 range_size(tmt::TensorMappingTranspose{T,R,D}, domain_size::NTuple{D,Integer}) where {T,R,D} = domain_size(tmt.tm, domain_size)
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35 domain_size(tmt::TensorMappingTranspose{T,R,D}, range_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, range_size)
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38
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39 struct TensorApplication{T,R,D} <: AbstractArray{T,R}
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40 t::TensorMapping{R,D}
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41 o::AbstractArray{T,D}
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42 end
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43
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44 Base.size(ta::TensorApplication{T,R,D}) where {T,R,D} = range_size(ta.t,size(ta.o))
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45 Base.getindex(ta::TensorApplication{T,R,D}, I::Vararg) where {T,R,D} = apply(ta.t, ta.o, I...)
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46 # TODO: What else is needed to implement the AbstractArray interface?
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47 import Base.*
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48 →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = TensorApplication(tm,o)
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49 # Should we overload some other infix binary operator?
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50 # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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51 *(args::Union{TensorMapping{T}, AbstractArray{T}}...) where T = foldr(*,args)
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52 *(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = TensorApplication(tm,o)
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53 *(scalar, ta::TensorApplication{T,R,D}) where {T,R,D} = scalar*ta.o
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54 *(ta::TensorApplication{T,R,D}, scalar::Number) where {T,R,D} = scalar*ta
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55 # We need to be really careful about good error messages.
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56 # For example what happens if you try to multiply TensorApplication with a TensorMapping(wrong order)?
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57
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58 # NOTE: TensorApplicationExpressions attempt to handle the situation when a TensorMapping
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59 # acts on a TensorApplication +- AbstractArray, such that the expression still can be
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60 # evaluated lazily per index.
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61 # TODO: Better naming of both struct and members
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62 # Since this is a lower layer which shouldnt be exposed, my opinion is that
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63 # we can afford to be quite verbose.
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64 struct TensorApplicationExpression{T,R,D} <: AbstractArray{T,R}
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65 ta::TensorApplication{R,D}
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66 o::AbstractArray{T,D}
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67 end
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68 Base.size(tae::TensorApplicationExpression) = size(tae.ta) #TODO: Not sure how to handle this
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69 Base.getindex(tae::TensorApplicationExpression, I::Vararg) = tae.ta[I...] + tae.o[I...]
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70 import Base.+
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71 import Base.-
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72 +(ta::TensorApplication{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = TensorApplicationExpression(ta,o)
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73 +(o::AbstractArray{T,D},ta::TensorApplication{T,R,D}) where {T,R,D} = ta + o
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74 -(ta::TensorApplication{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = ta + -o
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75 -(o::AbstractArray{T,D},ta::TensorApplication{T,R,D}) where {T,R,D} = -ta + o
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77 # NOTE: Another (quite neat) way to handle lazy evaluation of
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78 # TensorApplication + AbstractArray is by using broadcasting.
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79 # However, with the drafted implementation below a
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80 # TensorApplication+AbstractArray now results in a generic function and we would
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81 # then need to define TensorMapping*generic function which does not seem like a
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82 # good idea.
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83 # NOTE: Could one use MappedArrays.jl instead?
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84 #
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85 # # Lazy evaluations of expressions on TensorApplications
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86 # # TODO: Need to decide on some good naming here.
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87 # +(ta::TensorApplication,o::AbstractArray) = I -> ta[I] + o[I]
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88 # +(o::AbstractArray,ta::TensorApplication) = ta+o
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89 # *(scalar::Number,ta::TensorApplication) = I -> scalar*ta[I]
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90 # *(ta::TensorApplication,scalar::Number) = scalar*ta
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91 # -(ta::TensorApplication,o::AbstractArray) = ta + -o
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92 # -(o::AbstractArray + ta::TensorApplication) = -ta + o
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93
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94 struct TensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D} where K<:typeof(R)
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95 t1::TensorMapping{T,R,K}
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96 t2::TensorMapping{T,K,D}
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97 end
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98
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99 import Base.∘
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100 ∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = TensorMappingComposition(s,t)
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101
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102 function range_size(tm::TensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
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103 range_size(tm.t1, domain_size(tm.t2, domain_size))
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104 end
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105
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106 function domain_size(tm::TensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
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107 domain_size(tm.t1, domain_size(tm.t2, range_size))
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108 end
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109
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110 function apply(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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111 apply(c.t1, TensorApplication(c.t2,v), I...)
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112 end
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114 function apply_transpose(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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115 apply_transpose(c.t2, TensorApplication(c.t1',v), I...)
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116 end
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117
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118 # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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119
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120
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121 export apply
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122 export apply_transpose
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123 export range_dim
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124 export domain_dim
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125 export range_size
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126 export →
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127
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128
163
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129 # # Automatic dimension expansion?
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130 # struct TensorOperator1dAd2d{T,I} <: TensorOperator{T,2}
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131 # t::TensorOperator{T,1}
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132 # end
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133
162
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134 end #module